Structify-Net: Random Graph generation with controlled size and customized structure

TL;DR

Structify-Net is a Python library that generates random graphs with customizable size and structure, allowing control over randomness and enabling the creation of diverse network types beyond traditional models.

Contribution

It introduces a flexible framework for generating controlled, customizable random graphs with a new structure zoo and an implementation as a Python library.

Findings

Demonstrates control over network size and structure.

Provides a collection of novel network structures.

Analyzes small-world properties of generated networks.

Abstract

Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size -- number of nodes, edges -- and a customizable structure, beyond blocks and spatial ones, based on node-pair rank and a tunable probability function allowing to control the amount of randomness. We introduce a structure zoo -- a collection of original network structures -- and conduct experiments on the small-world properties of networks generated by those structures. Finally, we introduce an implementation as a Python library named Structify-net.